Seaborn
What it is: Statistical visualization library built on Matplotlib. Beautiful by default, simple syntax.
What It Does Best
Statistical plots. Distributions, correlations, regressions with one line of code.
Pandas integration. Pass DataFrames directly. Automatically handles aggregations and grouping.
Beautiful defaults. Looks professional without customization. Color palettes scientifically designed.
Key Features
Statistical plots: Distributions, regressions, categorical plots built-in
Pandas DataFrame support: Native DataFrame integration
Color palettes: Beautiful, perceptually uniform color schemes
FacetGrid: Easy multi-panel plots by categories
Built on Matplotlib: Full Matplotlib customization available
Pricing
Free. Open source, BSD license.
When to Use It
✅ Exploratory data analysis in Jupyter
✅ Statistical visualizations quickly
✅ You work with Pandas DataFrames
✅ Want good-looking plots without customization
✅ Need correlation and distribution plots
When NOT to Use It
❌ Need interactive charts (use Plotly)
❌ Extremely custom chart types (use Matplotlib)
❌ Non-statistical business charts
❌ Web-based visualizations
❌ Real-time data streaming
Common Use Cases
EDA: Exploratory data analysis in Jupyter notebooks
Statistical analysis: Distribution plots, box plots, violin plots
Correlation analysis: Heatmaps, pair plots, regression plots
Research visualization: Publication-ready statistical graphics
Data science workflows: Quick insights from Pandas DataFrames
Seaborn vs Alternatives
vs Matplotlib: Seaborn easier, better defaults; Matplotlib more control, verbose
vs Plotly: Seaborn better statistical plots; Plotly interactive, shareable
vs ggplot2: Similar philosophy; Seaborn for Python, ggplot2 for R
Unique Strengths
Statistical focus: Best library for statistical visualization in Python
One-line plots: Complex statistical charts in single function call
Beautiful defaults: Professional appearance without customization
Pandas native: Seamless DataFrame integration
Bottom line: Matplotlib made easy. Perfect for data scientists doing EDA. One-line statistical plots that look publication-ready.